Since most of the domain adaptation work focuses on the unsupervised setting, semi-supervised domain adaptation (SSDA) is not well studied. In [1]}, the standard unsupervised domain adaption (UDA) methods were shown to be less effective in the SSDA setting. [1]} introduced an iterative algorithm that alternates between minimizing and maximizing the entropy of the output. Our method is different and simpler compared to [1]}. We show that our method does not degrade in performance when going from unsupervised to semi-supervised setting, achieving state-of-the-art results for SSDA on DomainNet, introduced in [1]}.
